Computer animators increasingly use computer-modeling techniques to generate models of three-dimensional objects for computer-generated imagery. In some cases, computing devices use computer-modeling techniques to retarget (or transfer) a motion performed by a three-dimensional object's digital skeleton to a different three-dimensional object's digital skeleton. For example, some existing computer-modeling techniques retarget a motion performed by one humanoid digital skeleton to another humanoid digital skeleton using damped-least-square methods for inverse kinematics.
Despite making advances in retargeting motion, existing computer-modeling systems have a number of shortcomings. In particular, conventional computer-modeling systems are often inaccurate (e.g., unrealistic), inefficient, and inflexible. For example, some conventional computer-modeling systems require post-processing adjustments to retarget a motion performed by a skeleton. For example, some existing computer-modeling systems directly map coordinates for joints of a source skeleton to joints of a standard skeleton in a pre-processing stage. Such mapping assumes that the positions of end-effectors of both the source and standard skeletons (e.g., a hand or foot of a humanoid) are in the same position or that the segments between joints of both skeletons are the same length. Such a rigid approach limits conventional systems to retargeting motion between skeletons of the same size and/or introduces inaccuracies in modeling motion across different skeletons.
By contrast, some existing computer-modeling systems iteratively optimize a machine-learning model with hand-designed objectives for end-effectors to preserve the essence of a motion retargeted from one skeleton to another skeleton. For instance, the machine-learning model may adjust the position of end-effectors based on an algorithm or design from a computer animator. But such machine-learning models rely on humans to discover properties of a motion and transfer such properties from one skeleton to another. By relying on humans, such supervised machines often introduce inaccuracies and fail to identify important features of a motion or skeleton when retargeting a motion between different skeletons.
Because existing computer-modeling systems lack the technology to accurately retarget a motion between different skeletons, existing computer-modeling techniques often provide a tedious and user-intensive process. These computer-modeling techniques prompt computer animators to use individual editing tools to modify joint positions or joint rotations to match a source motion. In such cases, the additional user input for joint position and rotation adjustments further consumes computer-processing capacity and time.
In addition to the inaccuracies and inefficiencies of some existing machine-learning techniques to retarget motion, training a machine-learning model to retarget a motion can be expensive and unreliable. Data sets with a ground truth for a retargeted motion on a different skeleton are limited and difficult for computer animators to generate. Paired motion data for different skeletons (e.g., features for different skeletons performing the same motion) are difficult to find or generate, which undermines the feasibility and reliability of such machine-learning approaches.